MU-MIMO Communications with MIMO Radar: From Co-existence to Joint Transmission (1707.00519v1)

Abstract: Beamforming techniques are proposed for a joint multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single device acts both as a radar and a communication base station (BS) by simultaneously communicating with downlink users and detecting radar targets. Two operational options are considered, where we first split the antennas into two groups, one for radar and the other for communication. Under this deployment, the radar signal is designed to fall into the null-space of the downlink channel. The communication beamformer is optimized such that the beampattern obtained matches the radar's beampattern while satisfying the communication performance requirements. To reduce the optimizations' constraints, we consider a second operational option, where all the antennas transmit a joint waveform that is shared by both radar and communications. In this case, we formulate an appropriate probing beampattern, while guaranteeing the performance of the downlink communications. By incorporating the SINR constraints into objective functions as penalty terms, we further simplify the original beamforming designs to weighted optimizations, and solve them by efficient manifold algorithms. Numerical results show that the shared deployment outperforms the separated case significantly, and the proposed weighted optimizations achieve a similar performance to the original optimizations, despite their significantly lower computational complexity.

• The paper introduces innovative joint beamforming methods that harmonize radar and communication functionalities in dual-role MU-MIMO systems.
• It demonstrates that shared antenna deployments outperform separated approaches, achieving higher SINR and enhanced beampattern quality with reduced computational complexity.
• The study establishes a framework for sustainable spectrum utilization through adaptive optimization, paving the way for autonomous systems and AI-driven enhancements.

Overview of MU-MIMO Communications with MIMO Radar: From Co-existence to Joint Transmission

The paper under discussion offers a detailed exploration into multi-input-multi-output (MIMO) radar-communication (RadCom) systems, where a single device fulfills dual roles: radar detection and communication base station (BS) service. The authors investigate several beamforming techniques that adaptively manage antennas for concurrent radar and communication efforts, focusing on both separated and shared antenna deployments. Through a combination of null-space projection (NSP) and sophisticated optimization methods, the paper aims to reconcile the coexistence challenges between radar and communications over the same spectral bands.

Technical Specifics and Methodologies

The primary technical thrust of this research revolves around two operational frameworks:

1. Separated Deployment: This configuration involves a distinct segmentation of antennas allocated individually to radar and communication tasks. The radar pulses are mathematically refined to project into the null-space of the communication channels, thus nullifying their disruptive interference. The communication signals are then shaped to mimic the radar beampattern while ensuring downlink performance stipulations.
2. Shared Deployment: A more integrated approach is examined where the entire set of antennas serves both radar and communication functions, transmitting a unified waveform. Here, the research develops a joint optimization technique that jointly satisfies radar and communication SINR constraints, utilizing manifold optimization to achieve computational efficiency.

The paper methodically covers the co-design processes for such dual-functional systems while emphasizing efficient spectrum sharing paradigms. It balances the contradictory demands on beampattern quality necessary for radar and the SINR levels essential for robust communication. The manifold-based optimization subtly combines SINR constraints into the objective function, transcending typical computational burdens characteristic of conventional methods like semidefinite relaxation (SDR).

Comparative Evaluations and Results

The paper produces compelling numerical results indicating that the shared deployment paradigm significantly surpasses the separated deployment in terms of performance under similar resource constraints. Manifold optimization algorithms admirably diverge from SDR in handling non-convex constraints, marking computational complexity reductions without substantial performance drops.

Key highlights of the numerical assessments include:

• Significant Performance Gains: The shared antenna strategy yields superior beampattern quality and SINR enhancements vis-à-vis separated deployments.
• Computational Efficiency: The manifold optimization achieves equivalent performance with reduced computational demands compared to SDR, aligning closely with rank-1 solutions inherently absent in direct semidefinite methods.
• Operational Feasibility: The optimizations surpass rigid constraint methods, providing flexible and always feasible solutions adaptable to dynamic user-configurations and demands.

Implications and Prospectives

This sophisticated integration of radar and communication functions under a unified hardware platform introduces both practical and theoretical ramifications:

• Sustainable Spectrum Utilization: By equipping RadCom systems with joint functionalities, the approach significantly advances resource-efficient spectrum usage, addressing critical challenges posed by the impending scarcity due to the proliferation of connected devices.
• Future Enhancements in AI: This research paves the path for leveraging adaptive learning techniques and intelligent optimization within RadCom systems. Future pursuits could involve AI-driven algorithms capable of real-time adaptive policies for spectrum sharing and usage.
• Design of Autonomous Systems: The blend of radar and communication features informs the design of autonomous vehicles and intelligent transport systems, offering seamless integration and cohabitation of information utility with environment recognition.

In summary, the paper contributes nuanced strategies to optimize the dual-functionalities of RadCom systems, utilizing advanced optimization perspectives to enhance performance and efficiency. The findings advocate further investigative expansions into cross-disciplinary integration strategies, fortifying cooperative implication across wireless domains.